The REDI-NET consortium is envisioned as a long-term, phased initiative that serves as a global Center of Excellence to leverage, coordinate and integrate pre-existing and novel real-time xenosurveillance efforts to optimize pathogen discovery and to provide a one-stop-shop for actionable pathogen intelligence for decision-makers.
The seamless integration of environmental parameters and bio-/xeno-surveillance pathogen data into a newly developed REDI-NET data warehouse will facilitate the development of a comprehensive data analysis pipeline.
Phase I (currently active) leverages existing DoD, academic, and civilian partnerships and provides a solid foundation of excellence to build a unique electronically merged data pipeline (e-MERGE) and REDI-NET pathogen spillover alert dashboard, supported by Gold standard reach-back labs with field-ready hi-tech SOPs for standardized remote DNA-based pathogen detection. The REDI-NET alert dashboard, with built-in functionality and data quality assurance for downstream use in mathematical modeling, will inform of changes with the relative portions on the contributory components of each pathogen with regard to baseline data, and generate prior warning of potential spillover events in real-time, initiating response and mitigating risks to global citizens and deployed warfighters.
A central function of the e-MERGE analysis pipeline will be the development of novel risk maps and models.
Phase II (proposed FY22-24) will expand the sampling frame of the established complementary diagnostic workflow developed under Phase I into new ecologies or terrain.
Sampling expansion will serve to establish new mobile laboratories, for further reach of threat characterization, as well as, verify the correctness of Phase I threat forecast models through new/updated parameters to confirm emergence of targeted vectors and pathogens.
Phase III (proposed >FY25) will focus on establishing new regional satellite laboratories to expand upon Phase I and Phase II outputs and integrate new remote data acquisition technologies to assess emerging disease threats.
As data accrual improves, strategic sampling will become model-driven.